Specifically, we consider pre-trained sentence representations for proposing tasks, which have been proven useful for improving semi-supervised learning [1]}.
We use a pre-trained sentence embedding model [1]}, [3]} to embed all sentences in a corpus and cluster them.
To propose an \(N\) -way task, we first randomly sample \(N\)  cluster-ids and remap them to random consecutive integers \(\lbrace 1, \ldots , N\rbrace \) .
Then examples for each label are sampled from the corresponding cluster, creating a classification task for classifying the sentences into their underlying cluster labels.
Note that the step of remapping the cluster-ids ensures that the model cannot memorize the sentence to cluster mapping, which would lead to meta over-fitting [4]}.
